Title
Adaptive Regularized Noise Smoothing Of Dense Range Image Using Directional Laplacian Operators
Abstract
This paper proposes a adaptive regularized noise smoothing algorithm for dense range image using directional Laplacian operates, which preserves discontinuities and removes Gaussian and impulsive noise: simultaneously. In general. dense range data includes heavy noise such as Gaussian noise and impulsive noise. Although the existing regularized noise smoothing algorithm can easily smooth Gaussian noise, impulsive noise is not easy to remove from observed range data. In addition, in order to recover the problem such as artifacts on edge region in the conventional regularized noise smoothing of range data, the second smoothness constraint is applied through minimizing the difference between the median filtered data and original data. As a result, the proposed algorithm can effectively remove the noise of dense range data with directional edge preserving.
Year
DOI
Venue
2001
10.1117/12.424896
THREE-DIMENSIONAL IMAGE CAPTURE AND APPLICATIONS IV
Keywords
Field
DocType
noise smoothing, discontinuity-preserving, data fusion, regularization, dense range data, adaptive regularization
Value noise,Computer vision,Median filter,Noise measurement,Salt-and-pepper noise,Image noise,Smoothing,Artificial intelligence,Gaussian noise,Gradient noise,Physics
Conference
Volume
ISSN
Citations 
4298
0277-786X
2
PageRank 
References 
Authors
0.40
0
5
Name
Order
Citations
PageRank
Jeongho Shin112417.26
Yiyong Sun241428.70
woongchan jung320.40
Joonki Paik461171.87
Mongi A. Abidi51372104.38